Installing Libraries¶
%pip install torch
%pip install sentence_transformers
%pip install --upgrade pip
%pip install numpy pandas scikit-learn umap-learn cython
%pip install hdbscan --no-build-isolation
%pip install bertopic
%pip install vaderSentiment
%pip install accelerate
%pip install transformers==4.49.0
%pip install keybert sentence-transformers
%pip install bertopic[all]
%pip install wheel
%pip install cython
%pip install numpy
%pip install hdbscan
%pip install nltk
%pip install stanza
Requirement already satisfied: torch in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (2.7.0) Requirement already satisfied: filelock in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch) (3.18.0) Requirement already satisfied: typing-extensions>=4.10.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch) (4.12.2) Requirement already satisfied: sympy>=1.13.3 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch) (1.14.0) Requirement already satisfied: networkx in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch) (3.4.2) Requirement already satisfied: jinja2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch) (3.1.6) Requirement already satisfied: fsspec in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch) (2025.3.2) Requirement already satisfied: setuptools in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch) (78.1.0) Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sympy>=1.13.3->torch) (1.3.0) Requirement already satisfied: MarkupSafe>=2.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from jinja2->torch) (3.0.2) Note: you may need to restart the kernel to use updated packages.
[notice] A new release of pip is available: 25.0.1 -> 25.1.1 [notice] To update, run: python.exe -m pip install --upgrade pip
Requirement already satisfied: sentence_transformers in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (4.0.2) Requirement already satisfied: transformers<5.0.0,>=4.41.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence_transformers) (4.49.0) Requirement already satisfied: tqdm in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence_transformers) (4.67.1) Requirement already satisfied: torch>=1.11.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence_transformers) (2.7.0) Requirement already satisfied: scikit-learn in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence_transformers) (1.6.1) Requirement already satisfied: scipy in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence_transformers) (1.15.2) Requirement already satisfied: huggingface-hub>=0.20.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence_transformers) (0.30.1) Requirement already satisfied: Pillow in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence_transformers) (11.1.0) Requirement already satisfied: typing_extensions>=4.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence_transformers) (4.12.2) Requirement already satisfied: filelock in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (3.18.0) Requirement already satisfied: fsspec>=2023.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (2025.3.2) Requirement already satisfied: packaging>=20.9 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (24.2) Requirement already satisfied: pyyaml>=5.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (6.0.2) Requirement already satisfied: requests in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub>=0.20.0->sentence_transformers) (2.32.3) Requirement already satisfied: sympy>=1.13.3 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence_transformers) (1.14.0) Requirement already satisfied: networkx in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence_transformers) (3.4.2) Requirement already satisfied: jinja2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence_transformers) (3.1.6) Requirement already satisfied: setuptools in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence_transformers) (78.1.0) Requirement already satisfied: colorama in c:\users\konsa\appdata\roaming\python\python313\site-packages (from tqdm->sentence_transformers) (0.4.6) Requirement already satisfied: numpy>=1.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence_transformers) (2.2.4) Requirement already satisfied: regex!=2019.12.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence_transformers) (2024.11.6) Requirement already satisfied: tokenizers<0.22,>=0.21 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence_transformers) (0.21.1) Requirement already satisfied: safetensors>=0.4.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence_transformers) (0.5.3) Requirement already satisfied: joblib>=1.2.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from scikit-learn->sentence_transformers) (1.4.2) Requirement already satisfied: threadpoolctl>=3.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from scikit-learn->sentence_transformers) (3.6.0) Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sympy>=1.13.3->torch>=1.11.0->sentence_transformers) (1.3.0) Requirement already satisfied: MarkupSafe>=2.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from jinja2->torch>=1.11.0->sentence_transformers) (3.0.2) Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->huggingface-hub>=0.20.0->sentence_transformers) (3.4.1) Requirement already satisfied: idna<4,>=2.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->huggingface-hub>=0.20.0->sentence_transformers) (3.10) Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->huggingface-hub>=0.20.0->sentence_transformers) (2.3.0) Requirement already satisfied: certifi>=2017.4.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->huggingface-hub>=0.20.0->sentence_transformers) (2025.1.31) Note: you may need to restart the kernel to use updated packages.
[notice] A new release of pip is available: 25.0.1 -> 25.1.1 [notice] To update, run: python.exe -m pip install --upgrade pip
Requirement already satisfied: pip in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (25.0.1)
Collecting pip
Downloading pip-25.1.1-py3-none-any.whl.metadata (3.6 kB)
Downloading pip-25.1.1-py3-none-any.whl (1.8 MB)
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Installing collected packages: pip
Attempting uninstall: pip
Found existing installation: pip 25.0.1
Uninstalling pip-25.0.1:
Successfully uninstalled pip-25.0.1
Successfully installed pip-25.1.1
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: numpy in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (2.2.4)
Requirement already satisfied: pandas in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (2.2.3)
Requirement already satisfied: scikit-learn in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (1.6.1)
Requirement already satisfied: umap-learn in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (0.5.7)
Requirement already satisfied: cython in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (3.0.12)
Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from pandas) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from pandas) (2025.1)
Requirement already satisfied: tzdata>=2022.7 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from pandas) (2025.1)
Requirement already satisfied: scipy>=1.6.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from scikit-learn) (1.15.2)
Requirement already satisfied: joblib>=1.2.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from scikit-learn) (1.4.2)
Requirement already satisfied: threadpoolctl>=3.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from scikit-learn) (3.6.0)
Requirement already satisfied: numba>=0.51.2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from umap-learn) (0.61.2)
Requirement already satisfied: pynndescent>=0.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from umap-learn) (0.5.13)
Requirement already satisfied: tqdm in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from umap-learn) (4.67.1)
Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from numba>=0.51.2->umap-learn) (0.44.0)
Requirement already satisfied: six>=1.5 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)
Requirement already satisfied: colorama in c:\users\konsa\appdata\roaming\python\python313\site-packages (from tqdm->umap-learn) (0.4.6)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: hdbscan in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (0.8.40)
Requirement already satisfied: numpy<3,>=1.20 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from hdbscan) (2.2.4)
Requirement already satisfied: scipy>=1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from hdbscan) (1.15.2)
Requirement already satisfied: scikit-learn>=0.20 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from hdbscan) (1.6.1)
Requirement already satisfied: joblib>=1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from hdbscan) (1.4.2)
Requirement already satisfied: threadpoolctl>=3.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from scikit-learn>=0.20->hdbscan) (3.6.0)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: bertopic in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (0.17.0)
Requirement already satisfied: hdbscan>=0.8.29 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic) (0.8.40)
Requirement already satisfied: numpy>=1.20.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic) (2.2.4)
Requirement already satisfied: pandas>=1.1.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic) (2.2.3)
Requirement already satisfied: plotly>=4.7.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic) (6.0.1)
Requirement already satisfied: scikit-learn>=1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic) (1.6.1)
Requirement already satisfied: sentence-transformers>=0.4.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic) (4.0.2)
Requirement already satisfied: tqdm>=4.41.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic) (4.67.1)
Requirement already satisfied: umap-learn>=0.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic) (0.5.7)
Requirement already satisfied: scipy>=1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from hdbscan>=0.8.29->bertopic) (1.15.2)
Requirement already satisfied: joblib>=1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from hdbscan>=0.8.29->bertopic) (1.4.2)
Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from pandas>=1.1.5->bertopic) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from pandas>=1.1.5->bertopic) (2025.1)
Requirement already satisfied: tzdata>=2022.7 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from pandas>=1.1.5->bertopic) (2025.1)
Requirement already satisfied: narwhals>=1.15.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from plotly>=4.7.0->bertopic) (1.34.0)
Requirement already satisfied: packaging in c:\users\konsa\appdata\roaming\python\python313\site-packages (from plotly>=4.7.0->bertopic) (24.2)
Requirement already satisfied: six>=1.5 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from python-dateutil>=2.8.2->pandas>=1.1.5->bertopic) (1.17.0)
Requirement already satisfied: threadpoolctl>=3.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from scikit-learn>=1.0->bertopic) (3.6.0)
Requirement already satisfied: transformers<5.0.0,>=4.41.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers>=0.4.1->bertopic) (4.49.0)
Requirement already satisfied: torch>=1.11.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers>=0.4.1->bertopic) (2.7.0)
Requirement already satisfied: huggingface-hub>=0.20.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers>=0.4.1->bertopic) (0.30.1)
Requirement already satisfied: Pillow in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers>=0.4.1->bertopic) (11.1.0)
Requirement already satisfied: typing_extensions>=4.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers>=0.4.1->bertopic) (4.12.2)
Requirement already satisfied: filelock in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic) (3.18.0)
Requirement already satisfied: pyyaml>=5.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic) (6.0.2)
Requirement already satisfied: regex!=2019.12.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic) (2024.11.6)
Requirement already satisfied: requests in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic) (2.32.3)
Requirement already satisfied: tokenizers<0.22,>=0.21 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic) (0.21.1)
Requirement already satisfied: safetensors>=0.4.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic) (0.5.3)
Requirement already satisfied: fsspec>=2023.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub>=0.20.0->sentence-transformers>=0.4.1->bertopic) (2025.3.2)
Requirement already satisfied: sympy>=1.13.3 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (1.14.0)
Requirement already satisfied: networkx in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (3.4.2)
Requirement already satisfied: jinja2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (3.1.6)
Requirement already satisfied: setuptools in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (78.1.0)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sympy>=1.13.3->torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (1.3.0)
Requirement already satisfied: colorama in c:\users\konsa\appdata\roaming\python\python313\site-packages (from tqdm>=4.41.1->bertopic) (0.4.6)
Requirement already satisfied: numba>=0.51.2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from umap-learn>=0.5.0->bertopic) (0.61.2)
Requirement already satisfied: pynndescent>=0.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from umap-learn>=0.5.0->bertopic) (0.5.13)
Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from numba>=0.51.2->umap-learn>=0.5.0->bertopic) (0.44.0)
Requirement already satisfied: MarkupSafe>=2.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from jinja2->torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (3.0.2)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic) (3.4.1)
Requirement already satisfied: idna<4,>=2.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic) (2.3.0)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic) (2025.1.31)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: vaderSentiment in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (3.3.2)
Requirement already satisfied: requests in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from vaderSentiment) (2.32.3)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->vaderSentiment) (3.4.1)
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Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->vaderSentiment) (2.3.0)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->vaderSentiment) (2025.1.31)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: accelerate in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (1.6.0)
Requirement already satisfied: numpy<3.0.0,>=1.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from accelerate) (2.2.4)
Requirement already satisfied: packaging>=20.0 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from accelerate) (24.2)
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Requirement already satisfied: pyyaml in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from accelerate) (6.0.2)
Requirement already satisfied: torch>=2.0.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from accelerate) (2.7.0)
Requirement already satisfied: huggingface-hub>=0.21.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from accelerate) (0.30.1)
Requirement already satisfied: safetensors>=0.4.3 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from accelerate) (0.5.3)
Requirement already satisfied: filelock in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub>=0.21.0->accelerate) (3.18.0)
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Requirement already satisfied: tqdm>=4.42.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub>=0.21.0->accelerate) (4.67.1)
Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub>=0.21.0->accelerate) (4.12.2)
Requirement already satisfied: sympy>=1.13.3 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=2.0.0->accelerate) (1.14.0)
Requirement already satisfied: networkx in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=2.0.0->accelerate) (3.4.2)
Requirement already satisfied: jinja2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=2.0.0->accelerate) (3.1.6)
Requirement already satisfied: setuptools in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=2.0.0->accelerate) (78.1.0)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sympy>=1.13.3->torch>=2.0.0->accelerate) (1.3.0)
Requirement already satisfied: colorama in c:\users\konsa\appdata\roaming\python\python313\site-packages (from tqdm>=4.42.1->huggingface-hub>=0.21.0->accelerate) (0.4.6)
Requirement already satisfied: MarkupSafe>=2.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from jinja2->torch>=2.0.0->accelerate) (3.0.2)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.4.1)
Requirement already satisfied: idna<4,>=2.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->huggingface-hub>=0.21.0->accelerate) (2.3.0)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->huggingface-hub>=0.21.0->accelerate) (2025.1.31)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: transformers==4.49.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (4.49.0)
Requirement already satisfied: filelock in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers==4.49.0) (3.18.0)
Requirement already satisfied: huggingface-hub<1.0,>=0.26.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers==4.49.0) (0.30.1)
Requirement already satisfied: numpy>=1.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers==4.49.0) (2.2.4)
Requirement already satisfied: packaging>=20.0 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from transformers==4.49.0) (24.2)
Requirement already satisfied: pyyaml>=5.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers==4.49.0) (6.0.2)
Requirement already satisfied: regex!=2019.12.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers==4.49.0) (2024.11.6)
Requirement already satisfied: requests in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers==4.49.0) (2.32.3)
Requirement already satisfied: tokenizers<0.22,>=0.21 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers==4.49.0) (0.21.1)
Requirement already satisfied: safetensors>=0.4.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers==4.49.0) (0.5.3)
Requirement already satisfied: tqdm>=4.27 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers==4.49.0) (4.67.1)
Requirement already satisfied: fsspec>=2023.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub<1.0,>=0.26.0->transformers==4.49.0) (2025.3.2)
Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub<1.0,>=0.26.0->transformers==4.49.0) (4.12.2)
Requirement already satisfied: colorama in c:\users\konsa\appdata\roaming\python\python313\site-packages (from tqdm>=4.27->transformers==4.49.0) (0.4.6)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers==4.49.0) (3.4.1)
Requirement already satisfied: idna<4,>=2.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers==4.49.0) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers==4.49.0) (2.3.0)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers==4.49.0) (2025.1.31)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: keybert in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (0.9.0)
Requirement already satisfied: sentence-transformers in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (4.0.2)
Requirement already satisfied: numpy>=1.18.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from keybert) (2.2.4)
Requirement already satisfied: rich>=10.4.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from keybert) (14.0.0)
Requirement already satisfied: scikit-learn>=0.22.2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from keybert) (1.6.1)
Requirement already satisfied: transformers<5.0.0,>=4.41.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers) (4.49.0)
Requirement already satisfied: tqdm in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers) (4.67.1)
Requirement already satisfied: torch>=1.11.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers) (2.7.0)
Requirement already satisfied: scipy in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers) (1.15.2)
Requirement already satisfied: huggingface-hub>=0.20.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers) (0.30.1)
Requirement already satisfied: Pillow in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers) (11.1.0)
Requirement already satisfied: typing_extensions>=4.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers) (4.12.2)
Requirement already satisfied: filelock in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (3.18.0)
Requirement already satisfied: packaging>=20.0 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (24.2)
Requirement already satisfied: pyyaml>=5.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (6.0.2)
Requirement already satisfied: regex!=2019.12.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (2024.11.6)
Requirement already satisfied: requests in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (2.32.3)
Requirement already satisfied: tokenizers<0.22,>=0.21 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (0.21.1)
Requirement already satisfied: safetensors>=0.4.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (0.5.3)
Requirement already satisfied: fsspec>=2023.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub>=0.20.0->sentence-transformers) (2025.3.2)
Requirement already satisfied: markdown-it-py>=2.2.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from rich>=10.4.0->keybert) (3.0.0)
Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from rich>=10.4.0->keybert) (2.19.1)
Requirement already satisfied: mdurl~=0.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from markdown-it-py>=2.2.0->rich>=10.4.0->keybert) (0.1.2)
Requirement already satisfied: joblib>=1.2.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from scikit-learn>=0.22.2->keybert) (1.4.2)
Requirement already satisfied: threadpoolctl>=3.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from scikit-learn>=0.22.2->keybert) (3.6.0)
Requirement already satisfied: sympy>=1.13.3 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers) (1.14.0)
Requirement already satisfied: networkx in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers) (3.4.2)
Requirement already satisfied: jinja2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers) (3.1.6)
Requirement already satisfied: setuptools in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers) (78.1.0)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sympy>=1.13.3->torch>=1.11.0->sentence-transformers) (1.3.0)
Requirement already satisfied: colorama in c:\users\konsa\appdata\roaming\python\python313\site-packages (from tqdm->sentence-transformers) (0.4.6)
Requirement already satisfied: MarkupSafe>=2.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from jinja2->torch>=1.11.0->sentence-transformers) (3.0.2)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers) (3.4.1)
Requirement already satisfied: idna<4,>=2.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers) (2.3.0)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers) (2025.1.31)
Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: bertopic[all] in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (0.17.0)
Requirement already satisfied: hdbscan>=0.8.29 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic[all]) (0.8.40)
Requirement already satisfied: numpy>=1.20.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic[all]) (2.2.4)
Requirement already satisfied: pandas>=1.1.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic[all]) (2.2.3)
Requirement already satisfied: plotly>=4.7.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic[all]) (6.0.1)
Requirement already satisfied: scikit-learn>=1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic[all]) (1.6.1)
Requirement already satisfied: sentence-transformers>=0.4.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic[all]) (4.0.2)
Requirement already satisfied: tqdm>=4.41.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic[all]) (4.67.1)
Requirement already satisfied: umap-learn>=0.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from bertopic[all]) (0.5.7)
Requirement already satisfied: scipy>=1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from hdbscan>=0.8.29->bertopic[all]) (1.15.2)
Requirement already satisfied: joblib>=1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from hdbscan>=0.8.29->bertopic[all]) (1.4.2)
Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from pandas>=1.1.5->bertopic[all]) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from pandas>=1.1.5->bertopic[all]) (2025.1)
Requirement already satisfied: tzdata>=2022.7 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from pandas>=1.1.5->bertopic[all]) (2025.1)
Requirement already satisfied: narwhals>=1.15.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from plotly>=4.7.0->bertopic[all]) (1.34.0)
Requirement already satisfied: packaging in c:\users\konsa\appdata\roaming\python\python313\site-packages (from plotly>=4.7.0->bertopic[all]) (24.2)
Requirement already satisfied: six>=1.5 in c:\users\konsa\appdata\roaming\python\python313\site-packages (from python-dateutil>=2.8.2->pandas>=1.1.5->bertopic[all]) (1.17.0)
Requirement already satisfied: threadpoolctl>=3.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from scikit-learn>=1.0->bertopic[all]) (3.6.0)
Requirement already satisfied: transformers<5.0.0,>=4.41.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers>=0.4.1->bertopic[all]) (4.49.0)
Requirement already satisfied: torch>=1.11.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers>=0.4.1->bertopic[all]) (2.7.0)
Requirement already satisfied: huggingface-hub>=0.20.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers>=0.4.1->bertopic[all]) (0.30.1)
Requirement already satisfied: Pillow in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers>=0.4.1->bertopic[all]) (11.1.0)
Requirement already satisfied: typing_extensions>=4.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sentence-transformers>=0.4.1->bertopic[all]) (4.12.2)
Requirement already satisfied: filelock in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic[all]) (3.18.0)
Requirement already satisfied: pyyaml>=5.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic[all]) (6.0.2)
Requirement already satisfied: regex!=2019.12.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic[all]) (2024.11.6)
Requirement already satisfied: requests in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic[all]) (2.32.3)
Requirement already satisfied: tokenizers<0.22,>=0.21 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic[all]) (0.21.1)
Requirement already satisfied: safetensors>=0.4.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic[all]) (0.5.3)
Requirement already satisfied: fsspec>=2023.5.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from huggingface-hub>=0.20.0->sentence-transformers>=0.4.1->bertopic[all]) (2025.3.2)
Requirement already satisfied: sympy>=1.13.3 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic[all]) (1.14.0)
Requirement already satisfied: networkx in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic[all]) (3.4.2)
Requirement already satisfied: jinja2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic[all]) (3.1.6)
Requirement already satisfied: setuptools in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic[all]) (78.1.0)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from sympy>=1.13.3->torch>=1.11.0->sentence-transformers>=0.4.1->bertopic[all]) (1.3.0)
Requirement already satisfied: colorama in c:\users\konsa\appdata\roaming\python\python313\site-packages (from tqdm>=4.41.1->bertopic[all]) (0.4.6)
Requirement already satisfied: numba>=0.51.2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from umap-learn>=0.5.0->bertopic[all]) (0.61.2)
Requirement already satisfied: pynndescent>=0.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from umap-learn>=0.5.0->bertopic[all]) (0.5.13)
Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from numba>=0.51.2->umap-learn>=0.5.0->bertopic[all]) (0.44.0)
Requirement already satisfied: MarkupSafe>=2.0 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from jinja2->torch>=1.11.0->sentence-transformers>=0.4.1->bertopic[all]) (3.0.2)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic[all]) (3.4.1)
Requirement already satisfied: idna<4,>=2.5 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic[all]) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic[all]) (2.3.0)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\konsa\appdata\local\programs\python\python313\lib\site-packages (from requests->transformers<5.0.0,>=4.41.0->sentence-transformers>=0.4.1->bertopic[all]) (2025.1.31)
Note: you may need to restart the kernel to use updated packages.
WARNING: bertopic 0.17.0 does not provide the extra 'all'
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Import first dataset.¶
Reviews.csv contains more than 600.000 student reviews extracted from ratemyprofessor.com
import pandas as pd
file = "reviews_df.csv"
reviews_df = pd.read_csv(file, encoding='utf-8')
reviews_df.head()
| Department | Quality | Difficulty | Thumbs Up | Thumbs Down | Comment | Date | College | |
|---|---|---|---|---|---|---|---|---|
| 0 | Accounting | 5.0 | 2 | 0 | 0 | She is my recitation instructor. I have her a... | Jun 24th, 2010 | Baruch College |
| 1 | Accounting | 4.0 | 4 | 0 | 0 | Easy grader for HW's. Hard exams. Must use tex... | Dec 14th, 2009 | Baruch College |
| 2 | Accounting | 3.5 | 4 | 0 | 0 | had her for honors, shes not bad at all.. i th... | Nov 20th, 2009 | Baruch College |
| 3 | Accounting | 3.0 | 5 | 0 | 0 | mean grader. if ur not smart,dont take her. sh... | Jun 1st, 2009 | Baruch College |
| 4 | Accounting | 3.0 | 3 | 0 | 0 | Although she is pretty tough, she does want he... | May 29th, 2009 | Baruch College |
print("Total number of reviews:", len(reviews_df))
Total number of reviews: 656850
Data Cleaning¶
import torch
torch.cuda.set_device(0) # Ensure correct GPU
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[7], line 3 1 import torch ----> 3 torch.cuda.set_device(0) # Ensure correct GPU File c:\Users\konsa\AppData\Local\Programs\Python\Python313\Lib\site-packages\torch\cuda\__init__.py:529, in set_device(device) 527 device = _get_device_index(device) 528 if device >= 0: --> 529 torch._C._cuda_setDevice(device) AttributeError: module 'torch._C' has no attribute '_cuda_setDevice'
For the case of our project we excluded comments that:¶
- are empty/ have no input (NaN, whitespace etc.)
- are lest than 10 characters long (With this we exclude results such as 'good','bad' that do not give us only sentiment and not information about why this sentiment exists.
- Also we remove symbols. This will reduce noise for the implementation of our models in later stages.
- Comments made before 2016 are excluded from our research, as the university has undergone significant changes since then.
- Reviews with significantly higher numbers of thumbs down are excluded, as they are likely considered unhelpful by students. Specifically, we exclude reviews that have received at least three thumbs down and where the thumbs-down ratio exceeds 50% of the total feedback.
import re
#Data cleaning
comment_col='Comment'
# Drop empty or NaN comments
reviews_df = reviews_df.dropna(subset=[comment_col])
reviews_df[comment_col] = reviews_df[comment_col].astype(str)
# Remove whitespace-only comments
reviews_df = reviews_df[reviews_df[comment_col].str.strip() != ""]
# Remove short comments (you can tweak the length as needed)
reviews_df = reviews_df[reviews_df[comment_col].str.len() > 10]
# Remove comments that are mostly symbols or gibberish
def is_gibberish(text):
# Remove spaces and check if remaining text is mostly non-alphanumeric
cleaned = re.sub(r'\s+', '', text)
ratio = len(re.sub(r'\w', '', cleaned)) / len(cleaned) if len(cleaned) > 0 else 1
return ratio > 0.5
reviews_df = reviews_df[~reviews_df[comment_col].apply(is_gibberish)]
print(len(reviews_df))
651426
# Make sure the review_date column is in datetime format
reviews_df["Date"] = pd.to_datetime(reviews_df["Date"], errors='coerce')
# Filter out rows where the date is in 2015 or earlier
reviews_df = reviews_df[reviews_df["Date"] > "2015-12-31"]
print(len(reviews_df))
244936
# Ensure numeric types (in case they're read as strings)
reviews_df["Thumbs Up"] = pd.to_numeric(reviews_df["Thumbs Up"], errors="coerce").fillna(0)
reviews_df["Thumbs Down"] = pd.to_numeric(reviews_df["Thumbs Down"], errors="coerce").fillna(0)
# Avoid division by zero by replacing 0 with NaN temporarily
reviews_df["total_votes"] = reviews_df["Thumbs Up"] + reviews_df["Thumbs Down"]
reviews_df["thumbs_down_ratio"] = reviews_df["Thumbs Down"] / reviews_df["total_votes"]
reviews_df["thumbs_down_ratio"] = reviews_df["thumbs_down_ratio"].fillna(0) # Optionally fill back with 0
# Apply condition safely
condition = (
(reviews_df["Thumbs Down"] > 3) &
(reviews_df["thumbs_down_ratio"] > 0.5)
)
# Remove reviews that meet both conditions
reviews_df = reviews_df[~condition]
print(len(reviews_df))
237371
We ended up with 237.371 comments for our analysis after data cleaning
reviews_df.reset_index(drop=True, inplace=True)
reviews_df.head()
| Department | Quality | Difficulty | Thumbs Up | Thumbs Down | Comment | Date | College | total_votes | thumbs_down_ratio | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Communication Studies | 2.0 | 3 | 0 | 0 | her class wasn't too hard. she's is just very ... | 2024-10-16 | Baruch College | 0 | 0.0 |
| 1 | Communication Studies | 3.0 | 2 | 0 | 0 | Professor Merking is overall a nice professor ... | 2023-09-26 | Baruch College | 0 | 0.0 |
| 2 | Communication Studies | 3.0 | 2 | 0 | 0 | She is alright. No hw, she gives 3 tests which... | 2023-05-16 | Baruch College | 0 | 0.0 |
| 3 | Communication Studies | 3.0 | 2 | 0 | 0 | She is alright. No hw, she gives 3 tests which... | 2023-05-16 | Baruch College | 0 | 0.0 |
| 4 | Communication Studies | 5.0 | 3 | 0 | 1 | Prof Merkin is amazing! Those who complain abo... | 2023-03-30 | Baruch College | 1 | 1.0 |
Sentiment Analysis¶
In this section, we aim to identify the sentiment of each review using pre-trained models such as VADER (VaderSentiment) and the DistilBERT transformer.
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
# Initialize sentiment analyzer
analyzer = SentimentIntensityAnalyzer()
# Apply sentiment analysis
reviews_df['sentiment_score'] = reviews_df['Comment'].apply(lambda x: analyzer.polarity_scores(x)['compound'])
# Optional: classify as positive/neutral/negative
reviews_df['sentiment_label'] = reviews_df['sentiment_score'].apply(
lambda x: 'positive' if x > 0.05 else 'negative' if x < -0.05 else 'neutral'
)
import torch
from transformers import pipeline
# Load the sentiment analysis pipeline with the correct model
classifier = pipeline("sentiment-analysis", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english",device=0)
# Convert comments to list
comments = list(reviews_df['Comment'])
# Run batched sentiment analysis
results = classifier(comments, batch_size=8)
# Extract and assign results
reviews_df['sentiment_label_transformer'] = [r['label'] for r in results]
reviews_df['sentiment_score_transformer'] = [r['score'] for r in results]
2025-05-09 18:04:31.664844: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1746813871.851135 31 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1746813871.905001 31 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
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Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
model.safetensors: 0%| | 0.00/268M [00:00<?, ?B/s]
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Device set to use cuda:0
reviews_df.head()
| Department | Quality | Difficulty | Thumbs Up | Thumbs Down | Comment | Date | College | total_votes | thumbs_down_ratio | sentiment_score | sentiment_label | sentiment_label_transformer | sentiment_score_transformer | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Communication Studies | 2.0 | 3 | 0 | 0 | her class wasn't too hard. she's is just very ... | 2024-10-16 | Baruch College | 0 | 0.0 | -0.6905 | negative | NEGATIVE | 0.999262 |
| 1 | Communication Studies | 3.0 | 2 | 0 | 0 | Professor Merking is overall a nice professor ... | 2023-09-26 | Baruch College | 0 | 0.0 | 0.6953 | positive | POSITIVE | 0.996985 |
| 2 | Communication Studies | 3.0 | 2 | 0 | 0 | She is alright. No hw, she gives 3 tests which... | 2023-05-16 | Baruch College | 0 | 0.0 | -0.4222 | negative | POSITIVE | 0.996527 |
| 3 | Communication Studies | 3.0 | 2 | 0 | 0 | She is alright. No hw, she gives 3 tests which... | 2023-05-16 | Baruch College | 0 | 0.0 | -0.4222 | negative | POSITIVE | 0.998279 |
| 4 | Communication Studies | 5.0 | 3 | 0 | 1 | Prof Merkin is amazing! Those who complain abo... | 2023-03-30 | Baruch College | 1 | 1.0 | -0.4981 | negative | POSITIVE | 0.991580 |
To evaluate the accuracy of sentiment classification, we use the quality ratings provided in each review. These ratings range from 1 to 5, with a score of 1 indicating strong dissatisfaction and a score of 5 indicating high satisfaction with the class.
#Sentiment models evaluation
print(reviews_df[reviews_df['Quality'] == 1].groupby('sentiment_label').size())
print(reviews_df[reviews_df['Quality'] == 1].groupby('sentiment_label_transformer').size())
reviews_df = reviews_df.drop(['sentiment_score', 'sentiment_label','total_votes','thumbs_down_ratio'], axis=1)
print(reviews_df[reviews_df['Quality'] == 5].groupby('sentiment_label_transformer').size())
final_df=reviews_df
final_df.to_csv("final_df.csv", index=False)
sentiment_label negative 31790 neutral 2955 positive 10832 dtype: int64 sentiment_label_transformer NEGATIVE 42813 POSITIVE 2764 dtype: int64 sentiment_label_transformer NEGATIVE 7177 POSITIVE 120637 dtype: int64
Our analysis shows that the VADER sentiment model classified approximately 10,800 of the 1-star reviews as positive and 32,000 as negative. In contrast, the DistilBERT transformer model identified around 43,000 of these reviews as negative and only 2,700 as positive, which aligns more closely with the expected sentiment. Based on this performance, we have chosen to retain the DistilBERT model for our further analysis.
final_df
| Department | Quality | Difficulty | Thumbs Up | Thumbs Down | Comment | Date | College | sentiment_label_transformer | sentiment_score_transformer | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Communication Studies | 2.0 | 3 | 0 | 0 | her class wasn't too hard. she's is just very ... | 2024-10-16 | Baruch College | NEGATIVE | 0.999262 |
| 1 | Communication Studies | 3.0 | 2 | 0 | 0 | Professor Merking is overall a nice professor ... | 2023-09-26 | Baruch College | POSITIVE | 0.996985 |
| 2 | Communication Studies | 3.0 | 2 | 0 | 0 | She is alright. No hw, she gives 3 tests which... | 2023-05-16 | Baruch College | POSITIVE | 0.996527 |
| 3 | Communication Studies | 3.0 | 2 | 0 | 0 | She is alright. No hw, she gives 3 tests which... | 2023-05-16 | Baruch College | POSITIVE | 0.998279 |
| 4 | Communication Studies | 5.0 | 3 | 0 | 1 | Prof Merkin is amazing! Those who complain abo... | 2023-03-30 | Baruch College | POSITIVE | 0.991580 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 237366 | Physics | 3.0 | 4 | 0 | 0 | He is nice professor and care about the studen... | 2021-03-28 | Queensborough Community College | NEGATIVE | 0.583886 |
| 237367 | Physics | 5.0 | 1 | 1 | 0 | Great Professor! Very flexible and very unders... | 2018-12-20 | Queensborough Community College | POSITIVE | 0.999697 |
| 237368 | Physics | 5.0 | 1 | 0 | 0 | Amazing and easy A professor. If you want to g... | 2018-05-28 | Queensborough Community College | POSITIVE | 0.999863 |
| 237369 | Physics | 4.0 | 2 | 0 | 0 | Honestly , he's a shy guy. Took him for PH111 ... | 2016-08-30 | Queensborough Community College | POSITIVE | 0.674730 |
| 237370 | Physics | 5.0 | 1 | 0 | 0 | He's a pretty cool professor. Just do all his ... | 2016-05-26 | Queensborough Community College | POSITIVE | 0.999747 |
237371 rows × 10 columns
Word Frequency¶
import nltk
from nltk.corpus import stopwords
from collections import Counter
import re
nltk.download('stopwords')
# Basic stopwords
stop_words = set(stopwords.words('english'))
# Add custom stopwords for reviews
custom_stopwords = ['class', 'professor', 'he', 'she', 'his', 'her', 'very', 'really', 'one', 'get', 'prof',
'got', 'take', 'took', 'like','also','2','3','baruch']
stop_words.update(custom_stopwords)
# Combine all comments into 1 string
all_text = " ".join(final_df['Comment'].fillna("").astype(str).tolist())
# Tokenize & Clean
words = re.findall(r'\b\w+\b', all_text.lower())
# Remove stopwords
filtered_words = [word for word in words if word not in stop_words]
# Count frequency
word_freq = Counter(filtered_words)
# Top 30 words
print(word_freq.most_common(20))
[nltk_data] Downloading package stopwords to /usr/share/nltk_data... [nltk_data] Package stopwords is already up-to-date!
[('students', 49542), ('easy', 47793), ('work', 44473), ('great', 40222), ('good', 40200), ('gives', 37016), ('best', 36440), ('lot', 34269), ('lectures', 33924), ('exams', 31604), ('time', 31478), ('would', 31307), ('final', 29157), ('grade', 27807), ('hard', 25441), ('questions', 25313), ('assignments', 24404), ('well', 24061), ('help', 23953), ('makes', 23461)]
from sentence_transformers import SentenceTransformer
import pandas as pd
model = SentenceTransformer('all-MiniLM-L6-v2') # or any SBERT model
sentences = final_df['Comment'].fillna("").astype(str).tolist()
embeddings = model.encode(sentences, show_progress_bar=True)
from sklearn.cluster import KMeans
n_clusters = 7
kmeans = KMeans(n_clusters=n_clusters, random_state=42)
labels = kmeans.fit_predict(embeddings)
Batches: 0%| | 0/7418 [00:00<?, ?it/s]
print(labels)
from keybert import KeyBERT
kw_model = KeyBERT('all-MiniLM-L6-v2')
for cluster_id in set(labels):
cluster_texts = [text for text, label in zip(sentences, labels) if label == cluster_id]
joined_text = " ".join(cluster_texts)
keywords = kw_model.extract_keywords(joined_text, keyphrase_ngram_range=(1,3), top_n=5)
print(f"Cluster {cluster_id}: {[kw[0] for kw in keywords]}")
[5 3 5 ... 6 1 6] Cluster 0: ['class professor merkin', 'struggles classes professor', 'professor classes extremely', 'classes amazing professor', 'classes wonderful professor'] Cluster 1: ['lectures decent grading', 'grader lectures decent', 'lecture good quizzes', 'lectures quizzes decent', 'lecture grades fairly'] Cluster 2: ['mean hates shes', 'shes mean hates', 'awful lovely professor', 'lady ruin life', 'utterly dreadful woman'] Cluster 3: ['class online difficult', 'online class difficult', 'class online overwhelmed', 'online difficult class', 'online classes hard'] Cluster 4: ['struggling class professor', 'exams challenging professor', 'challenging class professor', 'professor going struggle', 'professor grades terribly'] Cluster 5: ['class difficult shes', 'class difficult teacher', 'class student struggled', 'class teacher difficulty', 'class struggling teacher'] Cluster 6: ['recommend professor rajkumar', 'enjoying lecture professor', 'lectures understanding professor', 'lectures insightful professor', 'lectures fantastic professor']
Since standard keyword extraction, topic modeling, and clustering techniques demonstrated limited results, I will attempt to reduce noise and improve topic identification by retaining only the key linguistic objects from each sentence in the comments. To do this I used stanza library.
#Runtime approx 6 hours with P100 accelerator
import stanza
import torch
import pandas as pd
from tqdm import tqdm
# Load your data
df = pd.read_csv("final_df.csv") # Replace with your actual path
df['objects_only'] = "" # Optional: create a column for output
# Download models (only once)
stanza.download('en')
# Initialize pipeline with GPU support
torch.cuda.set_device(0) # Ensure correct GPU
nlp = stanza.Pipeline(lang='en', processors='tokenize,mwt,pos,lemma,depparse', use_gpu=True)
# Process each comment
results = []
for idx, text in tqdm(zip(df.index, df['Comment']), total=len(df)):
try:
doc = nlp(text)
objects = [word.text for sent in doc.sentences for word in sent.words if word.deprel == 'obj']
results.append(" ".join(objects))
except Exception:
results.append("")
df['objects_only'] = results
df.to_csv("student_reviews_with_objects.csv", index=False)
To continue running notebook you can read the result dataset in the following cell
import pandas as pd
final_df = pd.read_csv("/kaggle/input/reviews-w-objects/student_reviews_with_objects.csv")
Word Frequency after reducing noise from text¶
import nltk
from nltk.corpus import stopwords
from collections import Counter
import re
nltk.download('stopwords')
# Basic stopwords
stop_words = set(stopwords.words('english'))
# Add custom stopwords for reviews
custom_stopwords = ['class', 'professor', 'he', 'she', 'his', 'her', 'very', 'really', 'one', 'get', 'prof',
'got', 'take', 'took', 'like','also','2','3','baruch']
stop_words.update(custom_stopwords)
# Combine all comments into 1 string
all_text = " ".join(final_df['objects_only'].fillna("").astype(str).tolist())
# Tokenize & Clean
words = re.findall(r'\b\w+\b', all_text.lower())
# Remove stopwords
filtered_words = [word for word in words if word not in stop_words]
# Count frequency
word_freq = Counter(filtered_words)
# Top 30 words
print(word_freq.most_common(50))
[nltk_data] Downloading package stopwords to /usr/share/nltk_data... [nltk_data] Package stopwords is already up-to-date!
[('lot', 18640), ('work', 16357), ('questions', 14282), ('students', 13366), ('grade', 13054), ('material', 11448), ('time', 11120), ('everything', 8817), ('attention', 8029), ('notes', 7866), ('feedback', 7778), ('assignments', 7546), ('course', 6736), ('homework', 6681), ('lectures', 6571), ('credit', 6198), ('us', 6181), ('things', 5915), ('classes', 5291), ('textbook', 5029), ('anything', 4953), ('best', 4265), ('exams', 4187), ('points', 4025), ('quizzes', 3755), ('grades', 3586), ('slides', 3525), ('something', 3520), ('effort', 3518), ('question', 3389), ('book', 3287), ('papers', 3156), ('chance', 2780), ('stuff', 2762), ('readings', 2701), ('help', 2665), ('math', 2652), ('everyone', 2637), ('problems', 2489), ('way', 2396), ('much', 2392), ('concepts', 2390), ('reviews', 2389), ('tests', 2349), ('paper', 2277), ('nothing', 2263), ('topics', 2203), ('sense', 2127), ('lots', 2116), ('lecture', 2048)]
Direct Clustering on reduced text¶
from sentence_transformers import SentenceTransformer
import pandas as pd
model = SentenceTransformer('all-MiniLM-L6-v2') # or any SBERT model
sentences = final_df['objects_only'].fillna("").astype(str).tolist()
embeddings = model.encode(sentences, show_progress_bar=True)
from sklearn.cluster import KMeans
n_clusters = 7
kmeans = KMeans(n_clusters=n_clusters, random_state=42)
labels = kmeans.fit_predict(embeddings)
Batches: 0%| | 0/7419 [00:00<?, ?it/s]
print(labels)
from keybert import KeyBERT
kw_model = KeyBERT('all-MiniLM-L6-v2')
for cluster_id in set(labels):
cluster_texts = [text for text, label in zip(sentences, labels) if label == cluster_id]
joined_text = " ".join(cluster_texts)
keywords = kw_model.extract_keywords(joined_text, keyphrase_ngram_range=(1,3), top_n=5)
print(f"Cluster {cluster_id}: {[kw[0] for kw in keywords]}")
[0 3 2 ... 5 5 6] Cluster 0: ['class homework professor', 'homework classes shes', 'class shes quizzes', 'professor classwork', 'material shes homework'] Cluster 1: ['em'] Cluster 2: ['lecture homework students', 'lecture homeworks exams', 'lecture homework quizzes', 'lecture homework tests', 'lecture people homework'] Cluster 3: ['paper quizzes attention', 'quiz tests attention', 'attention work quizzes', 'quizzes test attention', 'paper work quizzes'] Cluster 4: ['points lots classes', 'points lots class', 'class points lots', 'point class thoughts', 'lot class discussions'] Cluster 5: ['class notes hes', 'guy notes homework', 'homework class professor', 'notes quizzes professor', 'students class hes'] Cluster 6: ['subject words lot', 'lot homework care', 'word work lot', 'words lot material', 'effort brain lots']
Semi-Unsupervised Learning¶
Part 1 - PCA and Clustering using the reduced text¶
By annotating results from the previous keyword frequency results, I retained the categories that I considered most important. These labels are occured in the dataset at least 100 times to be considered important, and represent different aspects of the student experience.
I embedded the labels using the same model and calculated the cosine similarity between each label and the corresponding comment. This resulted to a dataframe with more than 100 labels.
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(device)
cuda
import pandas as pd
import numpy as np
import torch
from sentence_transformers import SentenceTransformer, util
from tqdm import tqdm
labels = ['work','questions','grade','material','time','attention','notes','feedback','assignments','homework','course','lectures','credit',
'classes','textbook','exams','quizzes','grades','slides','effort','book','papers','readings','help','tests','paper','topics','lecture',
'hw','fun','test','essays','gpa','exam','emails','assignment','information','examples','videos','subject','quiz','knowledge','instructions',
'answers','syllabus','attendance','chapters','opportunity','materials','accent','reading','final','opportunities','skills','attitude','midterm',
'essay','understanding','books','project','participation','projects','content','lessons','labs','advice','lab','mistakes','credits','teaching','style',
'discussions','email','criteria','passion','environment','powerpoints','homeworks','info','guide','score','expectations','money','articles','respect',
'presentation','resources','blackboard','phone','directions','curve','opinion','discussion','deadlines','sessions','powerpoint','group','presentations',
'guidelines','stress','grader','home','tools','response','format','textbooks','schedule','session','note','research','formulas','background',
'explanations','difficulty','patience','method','terms','terminology','formulas','career','midterms','posts','feedbacks','methods','hws','attempts',
'ability','scores','structure','explanation','humor','requirements','practice','policy','tutoring','instruction','exercises','classroom','guidance',
'curriculum','deadline','recordings','absences','pressure','finals','results','clarification']
# Load model and put it on GPU
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = SentenceTransformer('all-MiniLM-L6-v2', device=device)
# Encode labels once (on GPU)
label_embeddings = model.encode(labels, convert_to_tensor=True, device=device)
# Batch size (adjust depending on your GPU memory)
batch_size = 32
# Prepare list to collect results
similarities_list = []
# Process comments in batches
for i in tqdm(range(0, len(final_df), batch_size), desc="Processing Batches"):
batch_comments = final_df['objects_only'].iloc[i:i+batch_size].tolist()
# Encode batch (on GPU)
comment_embeddings = model.encode(batch_comments, convert_to_tensor=True, device=device)
# Compute cosine similarities (on GPU)
cosine_scores = util.cos_sim(comment_embeddings, label_embeddings)
# Move to CPU and convert to numpy
batch_similarities = cosine_scores.cpu().numpy()
similarities_list.append(batch_similarities)
# Stack all batches
all_similarities = np.vstack(similarities_list)
# Create DataFrame
similarities_df = pd.DataFrame(all_similarities, columns=[f"{label}_prob" for label in labels])
# Final merged DataFrame
final_df = pd.concat([final_df.reset_index(drop=True), similarities_df], axis=1)
# Done!
#final_df.to_csv('df_with_labels.csv', index=False)
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Labels of the dataframe after applying similarities:
print(list(final_df.columns))
['Unnamed: 0', 'Department', 'Quality', 'Difficulty', 'Comment', 'Date', 'College', 'sentiment_label_transformer', 'sentiment_score_transformer', 'objects_only', 'work_prob', 'questions_prob', 'grade_prob', 'material_prob', 'time_prob', 'attention_prob', 'notes_prob', 'feedback_prob', 'assignments_prob', 'homework_prob', 'course_prob', 'lectures_prob', 'credit_prob', 'classes_prob', 'textbook_prob', 'exams_prob', 'quizzes_prob', 'grades_prob', 'slides_prob', 'effort_prob', 'book_prob', 'papers_prob', 'readings_prob', 'help_prob', 'tests_prob', 'paper_prob', 'topics_prob', 'lecture_prob', 'hw_prob', 'fun_prob', 'test_prob', 'essays_prob', 'gpa_prob', 'exam_prob', 'emails_prob', 'assignment_prob', 'information_prob', 'examples_prob', 'videos_prob', 'subject_prob', 'quiz_prob', 'knowledge_prob', 'instructions_prob', 'answers_prob', 'syllabus_prob', 'attendance_prob', 'chapters_prob', 'opportunity_prob', 'materials_prob', 'accent_prob', 'reading_prob', 'final_prob', 'opportunities_prob', 'skills_prob', 'attitude_prob', 'midterm_prob', 'essay_prob', 'understanding_prob', 'books_prob', 'project_prob', 'participation_prob', 'projects_prob', 'content_prob', 'lessons_prob', 'labs_prob', 'advice_prob', 'lab_prob', 'mistakes_prob', 'credits_prob', 'teaching_prob', 'style_prob', 'discussions_prob', 'email_prob', 'criteria_prob', 'passion_prob', 'environment_prob', 'powerpoints_prob', 'homeworks_prob', 'info_prob', 'guide_prob', 'score_prob', 'expectations_prob', 'money_prob', 'articles_prob', 'respect_prob', 'presentation_prob', 'resources_prob', 'blackboard_prob', 'phone_prob', 'directions_prob', 'curve_prob', 'opinion_prob', 'discussion_prob', 'deadlines_prob', 'sessions_prob', 'powerpoint_prob', 'group_prob', 'presentations_prob', 'guidelines_prob', 'stress_prob', 'grader_prob', 'home_prob', 'tools_prob', 'response_prob', 'format_prob', 'textbooks_prob', 'schedule_prob', 'session_prob', 'note_prob', 'research_prob', 'formulas_prob', 'background_prob', 'explanations_prob', 'difficulty_prob', 'patience_prob', 'method_prob', 'terms_prob', 'terminology_prob', 'formulas_prob', 'career_prob', 'midterms_prob', 'posts_prob', 'feedbacks_prob', 'methods_prob', 'hws_prob', 'attempts_prob', 'ability_prob', 'scores_prob', 'structure_prob', 'explanation_prob', 'humor_prob', 'requirements_prob', 'practice_prob', 'policy_prob', 'tutoring_prob', 'instruction_prob', 'exercises_prob', 'classroom_prob', 'guidance_prob', 'curriculum_prob', 'deadline_prob', 'recordings_prob', 'absences_prob', 'pressure_prob', 'finals_prob', 'results_prob', 'clarification_prob']
Performing Principal Component Analysis (PCA) to reduce dimensionality and determining the optimal number of components by examining the cumulative explained variance.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
# --- Your DataFrame ---
# (make sure you load or define final_df before this)
# Specify your starting column
start_column = 'work_prob'
# Find index of start_column
start_idx = final_df.columns.get_loc(start_column)
# Select all columns after the start_column
columns_for_pca = final_df.columns[start_idx + 1:]
# Prepare data (only numeric columns, drop NaNs if needed)
X = final_df[columns_for_pca].select_dtypes(include=[np.number]).dropna()
# Check if X is empty
if X.shape[1] == 0:
raise ValueError("No numeric columns found after the specified start column.")
# Fit PCA
pca = PCA()
X_pca = pca.fit_transform(X)
# Explained variance
explained_variance_ratio = pca.explained_variance_ratio_
cumulative_variance = np.cumsum(explained_variance_ratio)
# Plot cumulative explained variance
plt.figure(figsize=(8, 6))
plt.plot(range(1, len(cumulative_variance) + 1), cumulative_variance, marker='o')
plt.xlabel('Number of Components')
plt.ylabel('Cumulative Explained Variance')
plt.title('PCA Explained Variance')
plt.grid(True)
plt.show()
#We will choose 10 components as it describes 75% of the variance.
pca = PCA(n_components=10)
X_pca = pca.fit_transform(X)
# X_pca now has shape (number_of_samples, 10)
# Optional: Save PCA results to a DataFrame
pca_df = pd.DataFrame(X_pca, columns=[f'PC{i+1}' for i in range(10)])
# If you want to attach it back to your original DataFrame
final_df_pca = pd.concat([final_df.reset_index(drop=True), pca_df], axis=1)
pca_df.head()
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | PC10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.245638 | 0.180714 | -0.221023 | 0.322108 | -0.165037 | -0.092894 | -0.007801 | -0.081985 | 0.007758 | 0.287692 |
| 1 | -1.436934 | -0.361553 | -0.057584 | 0.763694 | -0.228414 | -0.302517 | 0.127172 | 0.222630 | -0.213423 | 0.259219 |
| 2 | 0.514291 | 0.709100 | 0.057545 | 0.511209 | -0.174763 | -0.031684 | -0.113555 | 0.101319 | -0.098681 | 0.282336 |
| 3 | 0.439777 | 0.829811 | -0.046056 | 0.462712 | 0.053805 | 0.003133 | -0.018555 | 0.100676 | -0.173517 | 0.366785 |
| 4 | 0.079189 | 0.234170 | -0.020774 | -0.118356 | -0.329299 | 0.175446 | -0.098058 | 0.096107 | 0.201906 | -0.152528 |
# Assuming you already have:
# - pca (your fitted PCA model)
# - final_df (your original DataFrame)
# - columns_for_pca (the columns you used for PCA)
import pandas as pd
# 1. Get the loadings (feature contributions to each principal component)
loadings = pd.DataFrame(
pca.components_.T,
columns=[f'PC{i+1}' for i in range(pca.n_components_)],
index=final_df[columns_for_pca].columns
)
# 2. Function to print top features for each PC
def print_top_features_per_pc(loadings, top_n=15):
for pc in loadings.columns:
print(f"\nTop {top_n} features contributing to {pc}:")
top_features = loadings[pc].sort_values(key=abs, ascending=False).head(top_n)
print(top_features)
# 3. Run it for all 10 components
print_top_features_per_pc(loadings, top_n=15)
Top 15 features contributing to PC1: classes_prob -0.154978 classroom_prob -0.140785 lecture_prob -0.131693 homework_prob -0.130815 teaching_prob -0.129721 homeworks_prob -0.128179 assignment_prob -0.127797 assignments_prob -0.122146 grade_prob -0.117973 textbook_prob -0.117791 project_prob -0.115395 lectures_prob -0.114446 essay_prob -0.111184 subject_prob -0.111027 exam_prob -0.110518 Name: PC1, dtype: float32 Top 15 features contributing to PC2: classes_prob 0.206274 lectures_prob 0.202470 classroom_prob 0.185897 textbooks_prob 0.181004 lecture_prob 0.177835 assignments_prob 0.177634 syllabus_prob 0.173720 curriculum_prob 0.170568 textbook_prob 0.169321 tutoring_prob 0.159937 time_prob -0.158659 grades_prob 0.145716 exams_prob 0.136665 money_prob -0.134526 response_prob -0.132416 Name: PC2, dtype: float32 Top 15 features contributing to PC3: grades_prob -0.265772 grade_prob -0.262167 grader_prob -0.237692 gpa_prob -0.198967 presentations_prob 0.189515 feedbacks_prob 0.177979 discussions_prob 0.161399 presentation_prob 0.156826 topics_prob 0.155293 attendance_prob -0.150126 resources_prob 0.149068 feedback_prob 0.137256 score_prob -0.135851 articles_prob 0.125053 scores_prob -0.120262 Name: PC3, dtype: float32 Top 15 features contributing to PC4: classes_prob -0.278308 quizzes_prob 0.222737 quiz_prob 0.212409 lecture_prob -0.190992 tests_prob 0.187831 scores_prob 0.176579 classroom_prob -0.174636 readings_prob 0.173641 exam_prob 0.173066 exams_prob 0.161945 lectures_prob -0.159325 teaching_prob -0.157873 homeworks_prob 0.156367 test_prob 0.150748 questions_prob 0.147703 Name: PC4, dtype: float32 Top 15 features contributing to PC5: feedback_prob -0.264975 feedbacks_prob -0.262013 materials_prob 0.232290 material_prob 0.190682 formulas_prob 0.186051 formulas_prob 0.186051 formulas_prob 0.186051 formulas_prob 0.186051 textbook_prob 0.156209 paper_prob 0.149172 papers_prob 0.146416 grades_prob -0.144616 books_prob 0.140906 opinion_prob -0.139617 attention_prob -0.139009 Name: PC5, dtype: float32 Top 15 features contributing to PC6: powerpoint_prob 0.238069 powerpoints_prob 0.219425 assignments_prob 0.176499 deadlines_prob 0.175353 effort_prob 0.173785 questions_prob -0.171767 projects_prob 0.167357 books_prob -0.166646 quizzes_prob -0.161814 presentations_prob 0.161266 assignment_prob 0.160318 textbooks_prob -0.158858 deadline_prob 0.158758 presentation_prob 0.157897 essays_prob 0.155557 Name: PC6, dtype: float32 Top 15 features contributing to PC7: slides_prob 0.258428 presentation_prob 0.208597 presentations_prob 0.200320 lectures_prob 0.184220 career_prob -0.171681 session_prob 0.168346 grades_prob -0.165873 lecture_prob 0.162015 essay_prob -0.159524 advice_prob -0.158352 powerpoint_prob 0.140035 group_prob 0.139301 sessions_prob 0.136278 essays_prob -0.127092 curve_prob 0.124276 Name: PC7, dtype: float32 Top 15 features contributing to PC8: classes_prob -0.241347 deadlines_prob 0.213755 opportunities_prob 0.188994 deadline_prob 0.188463 schedule_prob 0.187276 sessions_prob 0.178184 materials_prob -0.177697 structure_prob -0.171268 material_prob -0.167202 method_prob -0.157826 attendance_prob 0.152589 style_prob -0.135841 grade_prob -0.134656 time_prob 0.129197 topics_prob -0.124073 Name: PC8, dtype: float32 Top 15 features contributing to PC9: questions_prob -0.232051 topics_prob -0.201380 quiz_prob -0.194867 guide_prob 0.187592 instructions_prob 0.185674 feedback_prob 0.174618 directions_prob 0.165549 instruction_prob 0.159985 feedbacks_prob 0.151607 participation_prob -0.144752 opportunities_prob -0.140299 textbook_prob 0.139378 lessons_prob 0.133581 guidance_prob 0.133565 quizzes_prob -0.133225 Name: PC9, dtype: float32 Top 15 features contributing to PC10: credit_prob -0.223183 credits_prob -0.215186 notes_prob -0.211415 method_prob 0.204563 methods_prob 0.195886 practice_prob 0.180541 papers_prob -0.179508 note_prob -0.168688 directions_prob 0.168071 difficulty_prob 0.163215 tutoring_prob 0.160847 instruction_prob 0.158615 reading_prob -0.157121 instructions_prob 0.152964 paper_prob -0.150175 Name: PC10, dtype: float32
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
# Try different number of clusters
inertia = []
K = range(1, 10) # Test k from 1 to 10
for k in K:
kmeans = KMeans(n_clusters=k, random_state=42)
kmeans.fit(X_pca)
inertia.append(kmeans.inertia_)
# Plot
plt.figure(figsize=(8, 6))
plt.plot(K, inertia, marker='o')
plt.xlabel('Number of clusters (k)')
plt.ylabel('Inertia')
plt.title('Elbow Method for Optimal k')
plt.grid(True)
plt.show()
/usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn(
from sklearn.cluster import KMeans
# Define number of clusters
n_clusters = 4
# Fit KMeans on your 10 PCA components
kmeans = KMeans(n_clusters=n_clusters, random_state=42)
cluster_labels = kmeans.fit_predict(X_pca)
# Optional: Create a DataFrame for cluster results
pca_df['cluster'] = cluster_labels # If you have a pca_df
# OR
# final_df_pca['cluster'] = cluster_labels # If you attached PCA to original df
# Quick check: number of points per cluster
print(np.bincount(cluster_labels))
/usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn(
[81470 68755 50546 36636]
pca_df
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | PC10 | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.245638 | 0.180714 | -0.221023 | 0.322108 | -0.165037 | -0.092894 | -0.007801 | -0.081985 | 0.007758 | 0.287692 | 0 |
| 1 | -1.436934 | -0.361553 | -0.057584 | 0.763694 | -0.228414 | -0.302517 | 0.127172 | 0.222630 | -0.213423 | 0.259219 | 3 |
| 2 | 0.514291 | 0.709100 | 0.057545 | 0.511209 | -0.174763 | -0.031684 | -0.113555 | 0.101319 | -0.098681 | 0.282336 | 0 |
| 3 | 0.439777 | 0.829811 | -0.046056 | 0.462712 | 0.053805 | 0.003133 | -0.018555 | 0.100676 | -0.173517 | 0.366785 | 0 |
| 4 | 0.079189 | 0.234170 | -0.020774 | -0.118356 | -0.329299 | 0.175446 | -0.098058 | 0.096107 | 0.201906 | -0.152528 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 237402 | -0.293803 | -0.063315 | -0.161420 | 0.220347 | -0.168907 | 0.222322 | -0.261293 | 0.018199 | 0.101693 | -0.017203 | 0 |
| 237403 | 0.356559 | 0.953208 | 0.025442 | 0.138864 | 0.294135 | 0.016914 | -0.182517 | 0.117201 | -0.064719 | 0.000135 | 0 |
| 237404 | 0.143879 | -0.520007 | -0.120729 | 0.001714 | 0.000043 | -0.007120 | 0.018714 | -0.108075 | 0.040783 | -0.047760 | 1 |
| 237405 | 0.116618 | -0.650743 | 0.268947 | 0.072335 | 0.093666 | 0.115173 | -0.081280 | 0.024438 | -0.056108 | 0.267761 | 1 |
| 237406 | -0.377857 | -0.206008 | -0.310988 | 0.378781 | 0.151291 | -0.112730 | 0.097784 | 0.074770 | 0.000110 | 0.051972 | 1 |
237407 rows × 11 columns
import pandas as pd
import numpy as np
# If you want to work with your PCA components
pca_df = pd.DataFrame(X_pca, columns=[f'PC{i+1}' for i in range(10)])
pca_df['cluster'] = cluster_labels
# Calculate mean for each cluster
cluster_summary = pca_df.groupby('cluster').mean()
print(cluster_summary)
PC1 PC2 PC3 PC4 PC5 PC6 PC7 \
cluster
0 -0.077084 0.451393 0.011973 0.002916 -0.013079 -0.000555 -0.017195
1 0.036293 -0.542030 -0.048198 -0.005592 0.034939 -0.032973 0.032487
2 0.825189 0.029089 0.048521 0.002016 -0.020884 0.030184 -0.011944
3 -1.035192 -0.026696 -0.003114 0.001229 -0.007674 0.021470 -0.006251
PC8 PC9 PC10
cluster
0 0.000318 0.007642 0.004058
1 0.014085 0.010353 -0.003490
2 -0.010736 -0.015221 0.000165
3 -0.012330 -0.015425 -0.002703
- Cluster 0: PC2,neg PC7 -- class/lectures/textbook/syllabus/career/advice
- Cluster 1: neg(PC2),neg(PC3),PC5,neg(PC4) --class/lecture/textbook/syllabus/grade/gpa/exams/presentation/discussion/feedback
- Cluster 2: PC1,PC3,PC6 --class/lecture/homework/assignments/grade/gpa/feedback/presentation/feedback
- Cluster 3: neg(PC1) --class/lecture/homework/assignment
Cluster interpretation:¶
- Cluster 0: Classes/Lectures,feedback,assignments
- Cluster 1: Grades/GPA,feedback,slides/presentation
- Cluster 2: Grades/GPA, homework/assignments
- Cluster 3: Material/Structure/Curriculum, Homework/Assignments
# Step 1: Drop all columns after 'objects_only'
cutoff_index = final_df.columns.get_loc('objects_only') + 1
final_df_trimmed = final_df.iloc[:, :cutoff_index]
# Step 2: Drop 'Unnamed: 0' column if it exists
if 'Unnamed: 0' in final_df_trimmed.columns:
final_df_trimmed = final_df_trimmed.drop(columns=['Unnamed: 0'])
# Step 3: Add the cluster labels from pca_df
final_df_trimmed['cluster'] = pca_df['cluster'].values # Ensure matching index
# Final result
final_df = final_df_trimmed
We end up with the following Dataframe which will be used for analytics
final_df.to_csv('final_df_with_clusters.csv', index=False)
cluster_df=final_df
final_df
| Department | Quality | Difficulty | Comment | Date | College | sentiment_label_transformer | sentiment_score_transformer | objects_only | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Communication Studies | 2.0 | 3.0 | her class wasn't too hard. she's is just very ... | 2024-10-16 | Baruch College | NEGATIVE | 0.999262 | it it class hole test you herself it | 0 |
| 1 | Communication Studies | 3.0 | 2.0 | Professor Merking is overall a nice professor ... | 2023-09-26 | Baruch College | POSITIVE | 0.996985 | tests | 3 |
| 2 | Communication Studies | 3.0 | 2.0 | She is alright. No hw, she gives 3 tests which... | 2023-05-16 | Baruch College | POSITIVE | 0.996527 | tests which assignment phones them review | 0 |
| 3 | Communication Studies | 3.0 | 2.0 | She is alright. No hw, she gives 3 tests which... | 2023-05-16 | Baruch College | POSITIVE | 0.998279 | tests which assignment phones them | 0 |
| 4 | Communication Studies | 5.0 | 3.0 | Prof Merkin is amazing! Those who complain abo... | 2023-03-30 | Baruch College | POSITIVE | 0.991580 | communication attention GPA it | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 237402 | Physics | 3.0 | 4.0 | He is nice professor and care about the studen... | 2021-03-28 | Queensborough Community College | NEGATIVE | 0.583884 | work lot report grade him | 0 |
| 237403 | Physics | 5.0 | 1.0 | Great Professor! Very flexible and very unders... | 2018-12-20 | Queensborough Community College | POSITIVE | 0.999697 | chapters assignments them assignments | 0 |
| 237404 | Physics | 5.0 | 1.0 | Amazing and easy A professor. If you want to g... | 2018-05-28 | Queensborough Community College | POSITIVE | 0.999863 | A him him | 1 |
| 237405 | Physics | 4.0 | 2.0 | Honestly , he's a shy guy. Took him for PH111 ... | 2016-08-30 | Queensborough Community College | POSITIVE | 0.674730 | him solutions that | 1 |
| 237406 | Physics | 5.0 | 1.0 | He's a pretty cool professor. Just do all his ... | 2016-05-26 | Queensborough Community College | POSITIVE | 0.999747 | labs A | 1 |
237407 rows × 10 columns
Part 1b- Analysis¶
cluster_df=pd.read_csv("final_df_with_clusters.csv")
cluster_df
| Department | Quality | Difficulty | Comment | Date | College | sentiment_label_transformer | sentiment_score_transformer | objects_only | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Communication Studies | 2.0 | 3.0 | her class wasn't too hard. she's is just very ... | 2024-10-16 | Baruch College | NEGATIVE | 0.999262 | it it class hole test you herself it | 0 |
| 1 | Communication Studies | 3.0 | 2.0 | Professor Merking is overall a nice professor ... | 2023-09-26 | Baruch College | POSITIVE | 0.996985 | tests | 3 |
| 2 | Communication Studies | 3.0 | 2.0 | She is alright. No hw, she gives 3 tests which... | 2023-05-16 | Baruch College | POSITIVE | 0.996527 | tests which assignment phones them review | 0 |
| 3 | Communication Studies | 3.0 | 2.0 | She is alright. No hw, she gives 3 tests which... | 2023-05-16 | Baruch College | POSITIVE | 0.998279 | tests which assignment phones them | 0 |
| 4 | Communication Studies | 5.0 | 3.0 | Prof Merkin is amazing! Those who complain abo... | 2023-03-30 | Baruch College | POSITIVE | 0.991580 | communication attention GPA it | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 237402 | Physics | 3.0 | 4.0 | He is nice professor and care about the studen... | 2021-03-28 | Queensborough Community College | NEGATIVE | 0.583884 | work lot report grade him | 0 |
| 237403 | Physics | 5.0 | 1.0 | Great Professor! Very flexible and very unders... | 2018-12-20 | Queensborough Community College | POSITIVE | 0.999697 | chapters assignments them assignments | 0 |
| 237404 | Physics | 5.0 | 1.0 | Amazing and easy A professor. If you want to g... | 2018-05-28 | Queensborough Community College | POSITIVE | 0.999863 | A him him | 1 |
| 237405 | Physics | 4.0 | 2.0 | Honestly , he's a shy guy. Took him for PH111 ... | 2016-08-30 | Queensborough Community College | POSITIVE | 0.674730 | him solutions that | 1 |
| 237406 | Physics | 5.0 | 1.0 | He's a pretty cool professor. Just do all his ... | 2016-05-26 | Queensborough Community College | POSITIVE | 0.999747 | labs A | 1 |
237407 rows × 10 columns
sentiment_map = {'NEGATIVE': 0, 'POSITIVE': 1}
cluster_df['sentiment_numeric'] = cluster_df['sentiment_label_transformer'].map(sentiment_map)
# Calculate total reviews and average sentiment per college
summary = cluster_df.groupby('College').agg(
total_reviews=('sentiment_numeric', 'count'),
avg_sentiment_score=('sentiment_numeric', 'mean')
).reset_index()
# Filter for colleges with more than 1000 reviews
summary_filtered = summary[summary['total_reviews'] > 1000].sort_values(by='avg_sentiment_score', ascending=False)
# Display the result
print(summary_filtered)
College total_reviews \
14 Hostos Community College 5785
2 Bronx Community College 6784
16 Kingsborough Community College 10582
17 LaGuardia Community College 16149
1 Borough of Manhattan Community College 24119
19 Medgar Evers College 3853
18 Lehman College 12371
12 College of Staten Island (CUNY - Staten Island) 13506
21 Queensborough Community College 12336
10 CUNY York College 6562
15 John Jay College of Criminal Justice 20093
11 City College of New York 17045
20 New York City College of Technology 12643
5 CUNY Queens College 27435
3 Brooklyn College 15004
0 Baruch College 31610
8 CUNY School of Professional Studies 1082
avg_sentiment_score
14 0.769231
2 0.760613
16 0.739936
17 0.712366
1 0.709897
19 0.680768
18 0.679977
12 0.674811
21 0.672503
10 0.671746
15 0.660728
11 0.648929
20 0.640433
5 0.638272
3 0.634031
0 0.631572
8 0.597967
import matplotlib.pyplot as plt
import seaborn as sns
cluster_df['sentiment_numeric'] = cluster_df['sentiment_label_transformer'].map(sentiment_map)
# Count number of reviews per college
college_counts = cluster_df['College'].value_counts()
# Filter colleges with more than 1000 reviews
valid_colleges = college_counts[college_counts > 1000].index
filtered_df = cluster_df[cluster_df['College'].isin(valid_colleges)]
# Calculate average sentiment score per college
avg_sentiment = filtered_df.groupby('College')['sentiment_numeric'].mean().sort_values()
# Convert to DataFrame for heatmap
avg_sentiment_df = avg_sentiment.to_frame().T # Transpose so colleges are columns
# Plot heatmap
plt.figure(figsize=(15, 2)) # Wide horizontal heatmap
sns.heatmap(avg_sentiment_df, annot=True, cmap='coolwarm_r', cbar_kws={'label': 'Average Sentiment Score'})
plt.title('Average Sentiment Score by College (Only Colleges with >1000 Reviews)')
plt.yticks([]) # Hide y-axis labels
plt.xticks(rotation=45, ha='right') # Rotate x-axis labels
plt.tight_layout()
plt.show()
/tmp/ipykernel_31/3026761609.py:24: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
cluster_df['sentiment_numeric'] = cluster_df['sentiment_label_transformer'].map(sentiment_map)
# Compute averages per college
college_stats = cluster_df.groupby('College').agg(
total_reviews=('sentiment_numeric', 'count'),
avg_sentiment=('sentiment_numeric', 'mean'),
avg_difficulty=('Difficulty', 'mean'),
avg_quality=('Quality', 'mean')
).reset_index()
# Filter for colleges with more than 1000 reviews
filtered_stats = college_stats[college_stats['total_reviews'] > 1000]
# Compute correlations
correlations = filtered_stats[['avg_sentiment', 'avg_difficulty', 'avg_quality']].corr()
# Display results
print("College Averages:")
print(filtered_stats.sort_values('avg_sentiment', ascending=False))
print("\nCorrelations:")
print(correlations)
College Averages:
College total_reviews \
14 Hostos Community College 5785
2 Bronx Community College 6784
16 Kingsborough Community College 10582
17 LaGuardia Community College 16149
1 Borough of Manhattan Community College 24119
19 Medgar Evers College 3853
18 Lehman College 12371
12 College of Staten Island (CUNY - Staten Island) 13506
21 Queensborough Community College 12336
10 CUNY York College 6562
15 John Jay College of Criminal Justice 20093
11 City College of New York 17045
20 New York City College of Technology 12643
5 CUNY Queens College 27435
3 Brooklyn College 15004
0 Baruch College 31610
8 CUNY School of Professional Studies 1082
avg_sentiment avg_difficulty avg_quality
14 0.769231 2.968885 4.045549
2 0.760613 2.852300 4.063016
16 0.739936 2.862880 4.006095
17 0.712366 3.034739 3.866741
1 0.709897 2.997015 3.871408
19 0.680768 3.076564 3.712821
18 0.679977 3.097001 3.791448
12 0.674811 3.041537 3.749334
21 0.672503 2.972033 3.750892
10 0.671746 3.087931 3.732703
15 0.660728 2.882646 3.775146
11 0.648929 3.152361 3.685186
20 0.640433 3.079491 3.635332
5 0.638272 3.173574 3.627738
3 0.634031 3.149294 3.648660
0 0.631572 3.096299 3.684499
8 0.597967 3.287431 3.556377
Correlations:
avg_sentiment avg_difficulty avg_quality
avg_sentiment 1.000000 -0.778975 0.976398
avg_difficulty -0.778975 1.000000 -0.824613
avg_quality 0.976398 -0.824613 1.000000
# Count reviews per college
college_counts = cluster_df['College'].value_counts()
# Filter colleges with more than 1000 reviews
valid_colleges = college_counts[college_counts > 1000].index
filtered_df = cluster_df[cluster_df['College'].isin(valid_colleges)]
# Cross-tab: cluster distribution per college as percentages
cluster_distribution = pd.crosstab(filtered_df['College'], filtered_df['cluster'], normalize='index') * 100
# Round to 2 decimal points
cluster_distribution = cluster_distribution.round(2)
print(cluster_distribution)
cluster 0 1 2 3 College Baruch College 33.32 28.58 23.19 14.91 Borough of Manhattan Community College 36.32 27.66 20.33 15.68 Bronx Community College 34.51 32.31 16.54 16.64 Brooklyn College 33.66 29.64 21.17 15.53 CUNY Queens College 34.19 28.62 22.44 14.75 CUNY School of Professional Studies 32.26 28.10 24.40 15.25 CUNY York College 35.45 27.57 21.72 15.27 City College of New York 33.70 28.68 23.20 14.41 College of Staten Island (CUNY - Staten Island) 33.45 29.75 21.75 15.05 Hostos Community College 32.55 32.05 18.50 16.91 John Jay College of Criminal Justice 34.99 27.87 21.62 15.52 Kingsborough Community College 33.50 30.71 18.99 16.80 LaGuardia Community College 34.69 29.72 19.34 16.24 Lehman College 36.04 27.22 21.32 15.41 Medgar Evers College 33.27 30.50 18.76 17.47 New York City College of Technology 33.24 29.95 21.15 15.67 Queensborough Community College 35.19 28.66 21.13 15.01
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(12, 6))
sns.heatmap(cluster_distribution, annot=True, fmt=".2f", cmap="Blues", cbar_kws={'label': '% of Comments'})
plt.title("Cluster Distribution per College (Only Colleges with >1000 Reviews)")
plt.xlabel("Cluster")
plt.ylabel("College")
plt.xticks(rotation=0)
plt.yticks(rotation=0)
plt.tight_layout()
plt.show()
cluster_df['sentiment_numeric'] = cluster_df['sentiment_label_transformer'].map({'NEGATIVE': 0, 'POSITIVE': 1})
cluster_metrics = cluster_df.groupby('cluster')[['Quality', 'Difficulty', 'sentiment_numeric']].mean().sort_index()
cluster_metrics.rename(columns={'sentiment_numeric': 'avg_sentiment'}, inplace=True)
print(cluster_metrics)
Quality Difficulty avg_sentiment cluster 0 3.748779 3.073033 0.665435 1 3.839852 2.990866 0.699949 2 3.567206 3.159696 0.614905 3 3.899511 2.961513 0.705754
Part 2- PCA and Clustering using the original text¶
import pandas as pd
final_df = pd.read_csv("student_reviews_with_objects.csv")
import pandas as pd
import numpy as np
import torch
from sentence_transformers import SentenceTransformer, util
from tqdm import tqdm
labels = ['work','questions','grade','material','time','attention','notes','feedback','assignments','homework','course','lectures','credit',
'classes','textbook','exams','quizzes','grades','slides','effort','book','papers','readings','help','tests','paper','topics','lecture',
'hw','fun','test','essays','gpa','exam','emails','assignment','information','examples','videos','subject','quiz','knowledge','instructions',
'answers','syllabus','attendance','chapters','opportunity','materials','accent','reading','final','opportunities','skills','attitude','midterm',
'essay','understanding','books','project','participation','projects','content','lessons','labs','advice','lab','mistakes','credits','teaching','style',
'discussions','email','criteria','passion','environment','powerpoints','homeworks','info','guide','score','expectations','money','articles','respect',
'presentation','resources','blackboard','phone','directions','curve','opinion','discussion','deadlines','sessions','powerpoint','group','presentations',
'guidelines','stress','grader','home','tools','response','format','textbooks','schedule','session','note','research','formulas','background',
'explanations','difficulty','patience','method','terms','terminology','formulas','career','midterms','posts','feedbacks','methods','hws','attempts',
'ability','scores','structure','explanation','humor','requirements','practice','policy','tutoring','instruction','exercises','classroom','guidance',
'curriculum','deadline','recordings','absences','pressure','finals','results','clarification']
# Load model and put it on GPU
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = SentenceTransformer('all-MiniLM-L6-v2', device=device)
# Encode labels once (on GPU)
label_embeddings = model.encode(labels, convert_to_tensor=True, device=device)
# Batch size (adjust depending on your GPU memory)
batch_size = 64
# Prepare list to collect results
similarities_list = []
# Process comments in batches
for i in tqdm(range(0, len(final_df), batch_size), desc="Processing Batches"):
batch_comments = final_df['Comment'].iloc[i:i+batch_size].tolist()
# Encode batch (on GPU)
comment_embeddings = model.encode(batch_comments, convert_to_tensor=True, device=device)
# Compute cosine similarities (on GPU)
cosine_scores = util.cos_sim(comment_embeddings, label_embeddings)
# Move to CPU and convert to numpy
batch_similarities = cosine_scores.cpu().numpy()
similarities_list.append(batch_similarities)
# Stack all batches
all_similarities = np.vstack(similarities_list)
# Create DataFrame
similarities_df = pd.DataFrame(all_similarities, columns=[f"{label}_prob" for label in labels])
# Final merged DataFrame
final_df = pd.concat([final_df.reset_index(drop=True), similarities_df], axis=1)
# Done!
#final_df.to_csv('df_with_labels.csv', index=False)
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
# --- Your DataFrame ---
# (make sure you load or define final_df before this)
# Specify your starting column
start_column = 'work_prob'
# Find index of start_column
start_idx = final_df.columns.get_loc(start_column)
# Select all columns after the start_column
columns_for_pca = final_df.columns[start_idx + 1:]
# Prepare data (only numeric columns, drop NaNs if needed)
X = final_df[columns_for_pca].select_dtypes(include=[np.number]).dropna()
# Check if X is empty
if X.shape[1] == 0:
raise ValueError("No numeric columns found after the specified start column.")
# Fit PCA
pca = PCA()
X_pca = pca.fit_transform(X)
# Explained variance
explained_variance_ratio = pca.explained_variance_ratio_
cumulative_variance = np.cumsum(explained_variance_ratio)
# Plot cumulative explained variance
plt.figure(figsize=(8, 6))
plt.plot(range(1, len(cumulative_variance) + 1), cumulative_variance, marker='o')
plt.xlabel('Number of Components')
plt.ylabel('Cumulative Explained Variance')
plt.title('PCA Explained Variance')
plt.grid(True)
plt.show()
#We will choose 10 components as it describes 75% of the variance.
pca = PCA(n_components=10)
X_pca = pca.fit_transform(X)
# X_pca now has shape (number_of_samples, 20)
# Optional: Save PCA results to a DataFrame
pca_df = pd.DataFrame(X_pca, columns=[f'PC{i+1}' for i in range(10)])
# If you want to attach it back to your original DataFrame
final_df_pca = pd.concat([final_df.reset_index(drop=True), pca_df], axis=1)
pca_df.head()
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | PC10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.220479 | -0.091875 | 0.316833 | -0.051618 | -0.058918 | 0.179161 | -0.152840 | 0.166463 | 0.066114 | 0.069672 |
| 1 | 0.691112 | -0.111693 | 0.206471 | -0.080795 | -0.017198 | 0.026949 | -0.007518 | 0.199412 | 0.065915 | 0.016454 |
| 2 | 0.304929 | 0.115624 | 0.260229 | 0.083505 | 0.051744 | 0.081797 | -0.093847 | 0.107122 | 0.049310 | 0.231794 |
| 3 | 0.327226 | 0.011078 | 0.225644 | -0.005435 | 0.032289 | 0.088180 | -0.097384 | 0.169312 | 0.046484 | 0.215482 |
| 4 | 0.038164 | -0.115254 | 0.040353 | -0.100589 | -0.054309 | 0.112195 | 0.076108 | -0.056310 | 0.029117 | 0.005458 |
import pandas as pd
# 1. Get the loadings (feature contributions to each principal component)
loadings = pd.DataFrame(
pca.components_.T,
columns=[f'PC{i+1}' for i in range(pca.n_components_)],
index=final_df[columns_for_pca].columns
)
# 2. Function to print top features for each PC
def print_top_features_per_pc(loadings, top_n=20):
for pc in loadings.columns:
print(f"\nTop {top_n} features contributing to {pc}:")
top_features = loadings[pc].sort_values(key=abs, ascending=False).head(top_n)
print(top_features)
# 3. Run it for all 20 components
print_top_features_per_pc(loadings, top_n=10)
Top 10 features contributing to PC1: homeworks_prob 0.145388 quizzes_prob 0.138905 textbook_prob 0.130173 topics_prob 0.126009 exam_prob 0.123768 lectures_prob 0.122766 instruction_prob 0.122722 books_prob 0.120268 resources_prob 0.120228 homework_prob 0.119782 Name: PC1, dtype: float32 Top 10 features contributing to PC2: tutoring_prob -0.206863 lectures_prob -0.198196 exams_prob -0.185684 lecture_prob -0.179379 textbooks_prob -0.173588 syllabus_prob -0.167377 textbook_prob -0.165699 classes_prob -0.163064 grades_prob -0.161355 classroom_prob -0.157187 Name: PC2, dtype: float32 Top 10 features contributing to PC3: exams_prob 0.221748 exam_prob 0.216943 grades_prob 0.214366 tests_prob 0.204830 test_prob 0.200993 lectures_prob -0.192415 scores_prob 0.187033 grade_prob 0.182502 score_prob 0.179274 lecture_prob -0.161719 Name: PC3, dtype: float32 Top 10 features contributing to PC4: grader_prob -0.242163 classroom_prob -0.240828 classes_prob -0.230124 grades_prob -0.221930 grade_prob -0.212572 attendance_prob -0.198566 directions_prob 0.194602 instructions_prob 0.188238 teaching_prob -0.180682 guide_prob 0.167086 Name: PC4, dtype: float32 Top 10 features contributing to PC5: quizzes_prob 0.227477 essay_prob -0.223752 lectures_prob 0.221110 essays_prob -0.195119 tests_prob 0.193090 sessions_prob 0.180346 test_prob 0.166261 lecture_prob 0.165301 assignments_prob -0.165170 exam_prob 0.163449 Name: PC5, dtype: float32 Top 10 features contributing to PC6: labs_prob -0.272904 lab_prob -0.231421 books_prob -0.213782 textbooks_prob -0.191792 presentations_prob 0.182396 presentation_prob 0.181233 readings_prob -0.163235 effort_prob 0.151403 research_prob -0.148849 career_prob -0.148510 Name: PC6, dtype: float32 Top 10 features contributing to PC7: schedule_prob 0.237718 attendance_prob 0.236130 explanations_prob -0.217977 requirements_prob 0.205895 understanding_prob -0.190459 explanation_prob -0.188764 teaching_prob -0.179123 deadline_prob 0.174043 participation_prob 0.165606 deadlines_prob 0.165598 Name: PC7, dtype: float32 Top 10 features contributing to PC8: essays_prob -0.250903 skills_prob 0.220026 essay_prob -0.216735 practice_prob 0.212846 tutoring_prob 0.191946 projects_prob 0.189581 career_prob 0.188591 discussions_prob -0.159441 teaching_prob 0.158419 discussion_prob -0.149532 Name: PC8, dtype: float32 Top 10 features contributing to PC9: lab_prob 0.247814 formulas_prob 0.230438 formulas_prob 0.230438 formulas_prob 0.230438 formulas_prob 0.230438 labs_prob 0.202996 slides_prob 0.182226 powerpoints_prob 0.181821 reading_prob -0.172788 powerpoint_prob 0.153661 Name: PC9, dtype: float32 Top 10 features contributing to PC10: powerpoint_prob 0.277634 powerpoints_prob 0.272390 slides_prob 0.195905 presentations_prob 0.185703 presentation_prob 0.163720 guidance_prob -0.157086 feedbacks_prob 0.155008 feedback_prob 0.150017 practice_prob -0.146455 terms_prob -0.144140 Name: PC10, dtype: float32
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
# Try different number of clusters
inertia = []
K = range(1, 10) # Test k from 1 to 10
for k in K:
kmeans = KMeans(n_clusters=k, random_state=42)
kmeans.fit(X_pca)
inertia.append(kmeans.inertia_)
# Plot
plt.figure(figsize=(8, 6))
plt.plot(K, inertia, marker='o')
plt.xlabel('Number of clusters (k)')
plt.ylabel('Inertia')
plt.title('Elbow Method for Optimal k')
plt.grid(True)
plt.show()
/usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn(
from sklearn.cluster import KMeans
# Define number of clusters
n_clusters = 3
# Fit KMeans on your 10 PCA components
kmeans = KMeans(n_clusters=n_clusters, random_state=42)
cluster_labels = kmeans.fit_predict(X_pca)
# Optional: Create a DataFrame for cluster results
pca_df['cluster'] = cluster_labels # If you have a pca_df
# OR
# final_df_pca['cluster'] = cluster_labels # If you attached PCA to original df
# Quick check: number of points per cluster
print(np.bincount(cluster_labels))
/usr/local/lib/python3.11/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning warnings.warn(
[ 62098 61538 113771]
import pandas as pd
import numpy as np
# If you want to work with your PCA components
pca_df = pd.DataFrame(X_pca, columns=[f'PC{i+1}' for i in range(10)])
pca_df['cluster'] = cluster_labels
# Calculate mean for each cluster
cluster_summary = pca_df.groupby('cluster').mean()
print(cluster_summary)
PC1 PC2 PC3 PC4 PC5 PC6 PC7 \
cluster
0 -0.591839 0.049099 0.013089 -0.009288 -0.000791 0.011894 -0.001157
1 0.593978 0.045060 0.012219 0.003914 -0.002289 0.014389 0.003050
2 0.001756 -0.051172 -0.013754 0.002952 0.001670 -0.014275 -0.001018
PC8 PC9 PC10
cluster
0 0.001306 -0.000344 -0.000703
1 -0.000807 -0.000025 -0.001035
2 -0.000276 0.000201 0.000943
I reject this clustering because clusters are very consistent/not clearly seperated. It does not give us valuable information as two classes are near perfect opposites and the rest PC components contribute very little to the result.